首页|基于EEMD-ELM的输电线路温度预测模型

基于EEMD-ELM的输电线路温度预测模型

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受负载、环境温度、风速、太阳辐射等多种因素影响,采用单一模型对输电线路温度进行预测的准确度较低.文章提出基于集合经验模态分解(EEMD)-极限学习机(ELM)的输电线路温度预测模型,并进行了试验验证.试验结果表明,相较BP神经网络模型、SVM模型,文章提出的预测模型的预测准确度较高.
A Temperature Prediction Model for Transmission Lines Based on EEMD-ELD
Due to many factors such as load,ambient temperature,wind speed,solar radiation,etc.,the accuracy of using a single model to predict the transmission line temperature is low.A temperature prediction model of transmission line based on ensemble Empirical Mode decomposition(EEMD)and Extreme Learning Machine(ELM)is proposed and verified by experiments.The experimental results show that compared with BP neural network model and SVM model,the prediction accuracy of the proposed model is higher.

transmission linestemperatureensemble empirical mode decompositionextreme learning machine

王新涛、郑强、牟磊、李宗、赵建坤

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青岛电气工程安装有限公司,山东青岛 266000

国网山东省电力公司青岛供电公司,山东青岛 266002

内蒙古电力科学研究院,内蒙古呼和浩特 010020

输电线路 温度 集合经验模态分解 极限学习机

2024

电力系统装备
《机电商报》社

电力系统装备

影响因子:0.008
ISSN:1671-8992
年,卷(期):2024.(7)